Ensemble Learning-Based Feature Selection for Phage Protein Prediction - Liu, Songbo; Cui, Chengmin; Chen, Huipeng; Liu, Tong (Frontiers in Microbiology, 2022)
Phage has high specificity for its host recognition. As a natural enemy of bacteria, it has been used to treat super bacteria for many times. Identifying phage proteins from the original sequence is very important for understanding the relationship between phage and host bacteria and developing new antimicrobial agents. However, the traditional experimental methods are expensive and time-consuming. Especially, the neural network needs a large number of accurate sample data. Therefore, we propose a feature selection method based on ensemble learning to classify phage proteins. In a data-driven way, effective features such as protein sequence characteristics, physicochemical features and spatial features are first selected. Then, an ensemble classification method is used to classify and predict a new sample based on the selected features. Experimental results which show better accuracies demonstrate the effectiveness of our method.